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. 2018 Jun 4;12(6):e0006517.
doi: 10.1371/journal.pntd.0006517. eCollection 2018 Jun.

Improving spatial prediction of Schistosoma haematobium prevalence in southern Ghana through new remote sensors and local water access profiles

Affiliations

Improving spatial prediction of Schistosoma haematobium prevalence in southern Ghana through new remote sensors and local water access profiles

Alexandra V Kulinkina et al. PLoS Negl Trop Dis. .

Abstract

Background: Schistosomiasis is a water-related neglected tropical disease. In many endemic low- and middle-income countries, insufficient surveillance and reporting lead to poor characterization of the demographic and geographic distribution of schistosomiasis cases. Hence, modeling is relied upon to predict areas of high transmission and to inform control strategies. We hypothesized that utilizing remotely sensed (RS) environmental data in combination with water, sanitation, and hygiene (WASH) variables could improve on the current predictive modeling approaches.

Methodology: Schistosoma haematobium prevalence data, collected from 73 rural Ghanaian schools, were used in a random forest model to investigate the predictive capacity of 15 environmental variables derived from RS data (Landsat 8, Sentinel-2, and Global Digital Elevation Model) with fine spatial resolution (10-30 m). Five methods of variable extraction were tested to determine the spatial linkage between school-based prevalence and the environmental conditions of potential transmission sites, including applying the models to known human water contact locations. Lastly, measures of local water access and groundwater quality were incorporated into RS-based models to assess the relative importance of environmental and WASH variables.

Principal findings: Predictive models based on environmental characterization of specific locations where people contact surface water bodies offered some improvement as compared to the traditional approach based on environmental characterization of locations where prevalence is measured. A water index (MNDWI) and topographic variables (elevation and slope) were important environmental risk factors, while overall, groundwater iron concentration predominated in the combined model that included WASH variables.

Conclusions/significance: The study helps to understand localized drivers of schistosomiasis transmission. Specifically, unsatisfactory water quality in boreholes perpetuates reliance on surface water bodies, indirectly increasing schistosomiasis risk and resulting in rapid reinfection (up to 40% prevalence six months following preventive chemotherapy). Considering WASH-related risk factors in schistosomiasis prediction can help shift the focus of control strategies from treating symptoms to reducing exposure.

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Conflict of interest statement

The authors have declared that no competing interests exist.

Figures

Fig 1
Fig 1. Map of the study area and spatial distribution of microhematuria (typical symptom of S. haematobium in school-aged children) prevalence created using the following data sources: Major rivers and town locations were obtained from CERSGIS, Accra, Ghana; hillshade relief surface was created from elevation data obtained from ASTER Global Digital Elevation Model (v2); mining locations were digitized from Sentinel-2 satellite imagery; microhematuria prevalence data were collected by A. Kulinkina.
Fig 2
Fig 2. Spatial definitions associated with the analysis conducted at the "community" level.
Fig 3
Fig 3. Modeling approach explaining raster data extraction methods to be matched to each point-prevalence location.
Fig 4
Fig 4. Schematic images of study communities showing settlements and water bodies (A1 and B1), NDWI values (A2 and B2), and MNDWI values (A3 and B3) generated using Landsat 8 data.
Fig 5
Fig 5. Predicted prevalence from Landsat 8 data (A) and Sentinel-2 data (B) for five extraction masks {1, 2, 4, 5, and 6}.
Surface water access points are shown as + symbols. The scale and extent of the image match Fig 4(B).
Fig 6
Fig 6. Predicted prevalence for the entire study area; for two smaller zoom windows (A and B); and variable importance values for the final model conducted with Landsat 8 environmental, topographic, and WASH variables.
The scales and extents of A and B match Figs 4 and 5. Surface water access points are shown as + symbols.

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